{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from time import time\n",
    "\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib import offsetbox\n",
    "from sklearn import (manifold, datasets, decomposition, ensemble,\n",
    "                     discriminant_analysis, random_projection, neighbors)\n",
    "from sklearn.cluster import KMeans\n",
    "\n",
    "digits = datasets.load_digits(n_class=6)\n",
    "X = digits.data\n",
    "y = digits.target\n",
    "n_samples, n_features = X.shape\n",
    "n_neighbors = 30\n",
    "n_digits = len(np.unique(digits.target))\n",
    "labels = digits.target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_embedding(X, title=None):\n",
    "    x_min, x_max = np.min(X, 0), np.max(X, 0)\n",
    "    X = (X - x_min) / (x_max - x_min)\n",
    "\n",
    "    plt.figure()\n",
    "    ax = plt.subplot(111)\n",
    "    for i in range(X.shape[0]):\n",
    "        plt.text(X[i, 0], X[i, 1], str(y[i]),\n",
    "                 color=plt.cm.Set1(y[i] / 10.),\n",
    "                 fontdict={'weight': 'bold', 'size': 9})\n",
    "\n",
    "    if hasattr(offsetbox, 'AnnotationBbox'):\n",
    "        # only print thumbnails with matplotlib > 1.0\n",
    "        shown_images = np.array([[1., 1.]])  # just something big\n",
    "        for i in range(X.shape[0]):\n",
    "            dist = np.sum((X[i] - shown_images) ** 2, 1)\n",
    "            if np.min(dist) < 4e-3:\n",
    "                # don't show points that are too close\n",
    "                continue\n",
    "            shown_images = np.r_[shown_images, [X[i]]]\n",
    "            imagebox = offsetbox.AnnotationBbox(\n",
    "                offsetbox.OffsetImage(digits.images[i], cmap=plt.cm.gray_r),\n",
    "                X[i])\n",
    "            ax.add_artist(imagebox)\n",
    "    plt.xticks([]), plt.yticks([])\n",
    "    if title is not None:\n",
    "        plt.title(title)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'A sampling from the 64-dimensional digits dataset')"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "n_img_per_row = 20\n",
    "img = np.zeros((10 * n_img_per_row, 10 * n_img_per_row))\n",
    "for i in range(n_img_per_row):\n",
    "    ix = 10 * i + 1\n",
    "    for j in range(n_img_per_row):\n",
    "        iy = 10 * j + 1\n",
    "        img[ix:ix + 8, iy:iy + 8] = X[i * n_img_per_row + j].reshape((8, 8))\n",
    "\n",
    "plt.imshow(img, cmap=plt.cm.binary)\n",
    "plt.xticks([])\n",
    "plt.yticks([])\n",
    "plt.title('A sampling from the 64-dimensional digits dataset')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Computing t-SNE embedding\n"
     ]
    },
    {
     "data": {
      "image/png": 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HAzRWKIJHCWdFryAuLg6QNmm7PXDFocM/vJHqf70cVN92u52kJJnX358rXZfQXe6qlyyh4k+PhLdvRZ9GCWdFryAtLc387HYHrjjU747bsZ3iNdVwB+x2Oy6XC03TSE5O9ts25vrrsCYFKAaj30gAhF1+HpCxiIG/uS+o8SgUwaBc6RTHJNHnnw/NzTTt2BHWfmvfeDNwo/Jy86Pmlp8rMxbTuGkTQ557NqzjUfRdlOas6BV4arSTJ7cvOt6Rwz+8gfq1wVa06hwxV6a2+R553rnm54gpvscmVIZHRRhRwlnRK/C0M2/Z4r/Ix9Dl/yGhaC8xF17YLWOpXdG2NkDDJ5+an81Qbi+ImJhuGY+ib6KEs6JX4GlnzsrK8jtxV3rV1RQlJFK/qbVsXvTFF5EYG4sQosuvxNhYACzDhwMw5PXXGLF5I8Nz19Dv5puAVq26X5qsvBW/fi0JRXtJKNrLoMwnfI7ZW0i6QuEPZXNW9ApSU1PNvBcBw7v1BEQNn7UWeq57/wPW2QeDvbUeb2TKLKLOOMPMgxF1/vnUr15NzJVXUrtiBVEXXED9qlUMeeVl6teupfrV19DKymjRA0oO//AGYq+9hpp/v2X2aWjVLeUVADS5ChGRkVjj4/2PWQiiL/g+1pEjqX3nneAOiqJPo7LSKXoUIURY0nqCNG9EnTUdgIpH/2wKYWtSEs2FhQxd/h9qli+n5uVXAKn1tjdZGMReew11n35GS3Exg557lgk//1nnc4B4kJSUhMvlAqTWXHr1tTR8+aUZaq6y2fUtupKVTmnO3UD7i1FdiN1L486dNOmCEACrtNY17dtnCmZo1Xo9/w/jv6rJdpo+y80HDpg5QLqKEMLzi9KaFZ1GCefuQAiizk+hafduWhoaOHj2TCJOO43Gb79VwjoMRJ45jYYNrU9wJbNmt1nfvFtWsHLfvaDNctup42n6ZnvbznTB2bBpk7moImNx2MbqTWtWKIJBTQh2A8JmY8DPfoZt/HhzWcRZ06G5GYCDZ8+k/vMvjtbwjnladD/jhk2bafhqmzkhl1C0l+G5a4g47TQA7E8+yYjNG83lUdPkU2WTq9AMtRY2GwPu/nm3jVVraqK5uLhNjuj2Y1AovKGEc5jQGhspuXwuRaNPoCghkf1JY2j4/HNzfb+bb8Y6atRRHGHvICkpKWSPiqZvvwOg4g9/4Mgd84FWb4iSWbNp/ErmdnbffTflv1sESO3aCPc+/MMbQgq1TklJITk5OajE/SIigtgf3QCRkW2WhzoGxfGPEs5dwKtblBBEf/98bCefbLazepguLNFRxHoEN5Red32fdKkywqi7+tpTXc05EVZG79/H6P37GLnuM4QQWCIjGb7SaS43XkNeeB4hRJtl50RYO7i9Gdqz4RrnD7vdTn5+PmvWrAn4e42nKEvcwNbtdY3en+udQqGEc1fQ7ZTRF14AwMEZ57A/aQyVf3mMSN17AKDZY7a/ubS0TReR0wNnVFN4J9SE/e29MBp37qSlrAwIbG7Iy8sjJycHIGCeDgNhs9FysDXfs/vuu5XWrAiImhAMkqDLKz3yJ6+LE889l83X/9D83rBW5iLWmpo4dEWq8uw4inhOKBq+zd7IzMwkIyODct3mnZKSElT/jTt3yglh3dwy9J23iQoiRF3Rt1F+zkESTv/cNthscrJICNC0Nr67Cu+E478ItL2vNm63m0GDBgEyzNyX9uy5fVFC25tt1PnnM/Rfy7oydMUxiqq+fSyiz+JbTwiQplIRNA6HwzQ9dCddzg3d1KQ8NRQBUcK5hxm6/D/E3HhDh+WeE0aKrpOXl0dhYaFZ/aQ30H6Csf5//1M2Z0VAlHAOkczMzDaJ4oOh9tXXumcwClMoOxyObunfbrebNQkDaeeeXj0QXJIklSBJYaCEc4i4XC6WLQveftiw7Wszw5knWl09AE37io6rR16HwxGSX3Nnhaxhaugu4QyQnZ2NpmlkZWX5b6h79cT84AfmIlP4OsZSlJBovsygJC/bKPomylsjRIwJIafTGVSV54oHH/S6vOnbbwHpZhVz2aUMfv658A3yKBLWHBUBSElJMYu4Buvm1hWC9dyx6MVfTfR5hROGDWPjZZfT8OFH5qqyX/6K5qIi02NHq60N65gVxx5Kcw4RQ1MLJiAhWFTS9racs/gj5jy6mqc+9F2Syu1243a7mTVrFoDp7tYdhOpnvefQISJPOgkAoc81RJx+elsNu6UFkD70hnatTBx9CyWcQyQzM5OlS5eSnZ0dVGFSwKsfbey11wSVtL0v0tyicUainVfWuti4+7DXNna7nby8PDIyMoDw3iy7A2GRl55WXQNA3dtvU/vuuwA07y8221nHjOn5wSl6BcqsEQZSUlIoLCwkLy/Pb2CC56y9EsByQi01NZXk5OSAk2uJQ2SVkoraRr/tUlJSSEpKCvpG6QsjB0i3ExVlulPOqHCzt7ISzj6rdf3+fa2fA7hbeuaQVhz7KM05RNxuNy6Xi6SkJDIyMsjJyQlZMPQF8vLymD1bRuYFEiiD+kWwfudhRg+OZeZJwwL27XK5Ak/WBdGHL7OEL9xuNxkZGQghArryNe2VQtfQoAH2VlaGNSxdcWyjhHOIZGVlMXv2bAoLC8nNzWX27Nnk5uYe7WH1erKzs5k0aRLp6ekBhcrgflFU1jaSefNUoiOtPTTCzuNyuVi8WOaCzs72XnHFoHbFCgC0yspuH5fi2ESZNUIkPT2d9PT0oz2MY4709HQcDgfp6ekBvVwOltex+OoziLRaqK5r8tv2aOF2u4POtQEgBg5Eq6gI+ziMUHGVo+XYR2nOih7H7XaTmZlpPvoHMkFU1Tfxy1c3c8Xjuby6ztXdw+sSLpeL5ORk4uLigmpvO3Fcp/fhcDhITk5WZrM+gtKcFT1ORkYGS5YsAeTjv91u99v+/1LGcfvsE83vfwhxsi4pKanL2/rCmNTMyMhg8eLFAYNgGvPyO9W/0+mksLCQwsJC3G53wGOmOPZRmrOix0lLS2PSpEkApKamkpqa6reqiKdghtAT9nenR4Ph9x7I+8Q6coT5OeKM0wP2u2CBrIc4a9asbo1+VPQeepXmXFBSxe/+s5U9h6uJjrByWXICl01O6LDs53NO6fGxhepa1R3a2rFKcnIyeXl5pl+y0+nE4XAEFWF5rBBIw20u2m9+btz6pc9+3G53UJOmiuOPHhPO3gSvp5AtKKninte3sL+slq+eTqOitJiPgLvb9eNtWXuSkpJY88VXYRXqyn80/CQnJwf0ajjWuPXWW3G5XKSlpYXF9OByuUxXzcLCwqDD0hu25CEiI7HGx4c8BsXRoduS7ReUVHHvG1vYW1qDBkRYBaeNtnPPZRP49xd7WL5hL0/Pm8a0sUMA2L6/gk0FR5h50lDGxg8IOR/DN0XlZn/e9qfoGXoiMX5P012FF0bv3+ezX6fTSWpqKnFxcT4nBNv3G3vtNSrYqZfQq5Lt1ze1MGFUHPbYCCwCGps1thSWcbiqnmljBwNto73GjxrIj85xhG3/C/61kSNV9YwZ3t/r/hSKo4m1k2YuwwskkDZupABQaQCOfcJm1igoqeK+1/PYc7gaDYi0Wfj+hHhuOncsFgss+UAmrfl8Vylrvys1o73amzsmnzAoLOOpbWjmlbUukpPs/CNnV9DRZQpFVwm6ziQwct1nHZZFRUX5bJ+cnMykSZPIz89X3hp9hJA154KSKm5+di3znl9HcXktY4b1o3+0jYamFt7fWszWPUdwbtqHVd/Tf786SEVNa7RXwaEqyqobaNE0qusaydle4nU/TqcTIYSZ4CYQk5OkkH/0nW/a7E/RsxgTqV19HUsTqaFmq6uvr/fZt3Hea5qmBHMfIWThXN/UwkVnjGLZ/BlcNjmB3YeqmTCq1RE/d/sh9hyqBk3anSvrGsjwiPYaNSiWG2Y4eO7W6disvoezdOlSkpKSSElJMTOP+ePLvdIuV1XX2GZ/ip6lN7u9dRVfNxyFIpyEbNYYP2ogF559Ovv27jGXfR5gm2n3t/2elJTEyx9+QV1ji89tjFnq9PR0M2GOP2oamgGoa2zhp1kbgI7BDApFV/B1w1ACWhFOwmJz3rd3T8iz139fswurRTCkf6TXNoa2nJOTYwYwBOKO2eO4/mwH/aJtpm375c8Kjqq/tKJvYoSsL168mIKCAhVIcoxxaNdhlv/yfdDg9uU3YvHzlB8uuiycPSfywsG3ByoZEG3jR+eMYaWfdjk5OUHb3F5Ys4u6xhZ+cuHJpvnFcK17Za2LnG8OUlpVr4S1oltxOp0sWrTILKHlTzCrQKfeybp/bsJiFbQ0abx41as9IqC71LsRMLK7pAqbJTwDbNE0ymsbeeL97X7bZWdnk5ycjNPpDCoBTIRNjs9w1fN0rRs7rD/xcdFU1jXyyloXv30z8ESjQtEZjPB0l8tlltDyx75Ro83X4TvmH/P2+Z7m0K7DPJ/6Ms/PfZmWZt9m0k71N/dlir8qwerhUBCOvgPRJcla39RC6tREXr5rJlMc3l3f3G43ycnJ2O32sJw0breb1NRU8vPzWbJkCampqX5TdUbaBP2irCz/Yg+zHv7YrEFXVddoutbddO4YzhhtRyC1lf9uO8h5D33Upr1CEQoZGRksWrQIl8vVqZSioGpJdoV1/9yE1RYehfHQrsMs/8X75vfGmp51KOiSWWP8qIFE2Sz87j9b2V3iPVl4dna2KZQ900P644/XTeL8iSMQD3Vcl5ubi9PpJCkpibS0NBwOB6mpqT77amjSGGWPxlVazR2zx/HGemnKeG2diyiblX/9ZCajB8fiOlTF6q8PYrEIquqaaGzW+NFZJ1Db2MIra13MOGlom6jCT7aX8Nt/59PQ1ILQj8XS+TMC/jZF3yQ5OblDyHVeXl7AMGwV3SfpjO94e+603czwuHgemvVYl8wQazLX+lx3aNdh3v7Nx91qg+6ccO4/jOue+sQMybZaBL7mAfPy8khPT8flcgXM0AUwINrmN0hk7ty5nZp0/Pyhi0yvkc+9CPvEB32MY8hIWmasZtrYwSzfsLdDVGF1fRNnjh1C6tTRPLPqW77ZX8ErnxXwo3NUIU5FcPgzx3nWmVS0+o53FcOGX7rrCCvu+cCnMG0/4Vewbg/uogpiBkVTW1bXod/Pl23BarPQ7MfDLFQ6J5wtEcw4cRhnjG5k9bYD1PgZ2OzZs0lKSiI9PT2oCbzKuia+2ucOW+6Lrv6hQgiumX4C97y+pUNUYUFJFa+sdbHncDVf7XMzyi4fO0srfQcPKBQGxnUQqBCwIvysz9rsU5i2187vtN0cuMO3Wz/+adwD3WLv75wuXrGfhReP59LJCQFnlY30j7m5uaSlpQXVfUlFxzvU0eDBt7Z6jSo0PD6y7pjBjBOH8s3+CiwC5p039iiOVnGsYES2qkom3gn3ZJ4nlYeqcZzVtmyXsb9QIzu7K51rl2zO1fVNVOtBHr5wOp3MmzePuLg4v7ZhT36/4isezv6qK0MKK3tKq3n0hslEWi1s2+fmkZVfs7ukEuN8eeqj1onCW783Fns/777ZCoUnWVlZIVcF70lCsfeCdO3rjEZpTOYFaypwu92kpaUFlXZ2/IXj2PjKVgBevOpVUv8yh+xffxj02I4GnbZiV9U18uKaXdgsAqvFt/ZcVlZGeXk5IN2JUlJSAgppIaRZYcCQkWHPx+B0OklJSQlKa6mqb+KnWRu44vFc3t5cxEVnjOLqMxMZEN32XhZhFUwbO4SS8t6h8SsU4aQnNcqCdXuoKmnVbg2t1h9ZWVlB57c2BLNBzpPrgh7b0aJzmrMtip9mbeC7A5VYLYKmZt923dTUVNNjIzc3F8Cvn2eEVfDkzVPJ+qSA5p8u5fwJw1n9dYnpwbH66wPc/0Y+I+3RHKluIMpmBU2jUs+XMXxgFGU1DXzyuzle79bz5s3DbreTlZUVsFp2XGyE16CUZMdg7n+jtfZbY7PGXUs3MDlpEM/eNt1vnwpFXyVQdF1zUwufv7SFs+ZNYc+mIkDaiC1+lD+3221ey8b13pmoy/L9lcSfMpSDO0r9tktLSyMnJ+eo+JB3SjiLgaPYUSxd51r8CGaQkx+dqXLR2Kzx69e2UFUvzSXrdx4mPi6af+TsImP5VpqYMG+HAAAgAElEQVSaNWIjraROTWTa2MH88pXNuGtb/Q4PVviflHM4HOTk5AT0jwYYPjC6gxtdVV0jL67eic0CTfpT199/PJ0xwwbQL7pXVftSKHoVgcwV2z/6jqgBUYyZkciejVI4V5ZUI/wI56ysLLPijOF04CshmrAKtGZNRvjpcktr1ug/vJ9f4ex2u1m2bBnz5s0L8peGl06ZNbQjBaxfPIe/pZ3ZLYMxBLMQMnHR3CkJfH/iCBIH9yPSZqWmoZmxw/vz1/e+oaaho0P4dB+eHkbZ+mADYr47IG9AhhtdVV0jd7+0iZKKWlMwA/z471/w6rrA/Sn6Bn0pPWpeXh5CiIDpe/NWbKP4qxKvgtkwXXz6/AZKdpTy4lWv8u2a3QDUHKn1qQA6nU4WLlxomkmXLFniV2vW9H5ih7QN6tn1iX+zi6HEBROj0R10SeWbOmYwWfNnsKngCAKNH+l+xKFMIHz+0EUMGDKSCT9davpOx0ZaeX/rAQ5V1nHTuQ7+vmYXX+4t4+uiijbbGtnmquoaeSqtY98Oh8O8qwZbWj46wsLMk4ZRXdfE3S9tpPBQJTUNcmBXTEnge6cMZ/TgWBzD+3fp9yqOP/pS+LQhuAJdRxtf3WpqriAn4xCABiMnDjc16lkLZpC7pNUOPDjJzuHdZV77jIuLIy4ujmXLlpk3h2CcDqpKaoL5aYDM4bNs2TIWLVpk5tIuLCzs0SLEXX4eHz9qIKMHx/B/L7YmCA2Xw7hB5offmp//vmYXIM0fExLiOFRRx7O3TWf04FhAare3Pr/eZ98Oh4OsrCzi4uKC8ruua2zh/D+uIibSRnV9Wy397c1FvL25iBFx0WT/InC+BIXieCIjI8OcR3K5XP611haNMWcnsvuz1pTCwgJaMxRvK2HsOSew+7M97Fi1E2tEq+nDl2AGzIl9w1/cmE8KJ4asyMjIMEPvU1NTjw3hbGiUR6rqiRs2slty2VoExEbaaGxpob6xhfsun8Dbm/ex70gNf7p+Mgfctdzz2haKjtTQ1KL5vTGkp6ezZMkS4uLiTBuVEQbenrNPHML6nYdp0WCKYxCf7DikisMqFGCmPY2Li6O8vJyMjAy/80stTS1tBDNIwWxgjZBxBOVFFaZgvuJPF9JQ3ciW5dvaBHu0x263U15eHjCOwp4wELfH03bs4BhqjtT63cblclFeXm5aAyZNmtTj5o0uC+ftxRWmeWH8XUsBaZoIlX6RFmKiIqhvbGbx1Wfw++wvqaqWf1phabW5TyOB/vSxQ7hxpiOgf3RaWpppe87OzsZut5OSkuJVOK/fedj8vPY7OWHwxvpChvSPalPvUKUZVfQ1DFdUw03WyFPti8sevoD3MlbT0uR9MvC7nAIAIvtFUlfRgNaiMezEIdgibSSdORoe8T0Wh8MR1JO6pmnYE+Nw75VjDiSYAdMTxDCbZGVl9Xh5sK6bNUYOZELCQFOLTRwcS7yXHBYg77aGzTc7O9tv6Gp1QwvVDdLzYtVXByirbs1t8dq6Qp/VTA6U15qmD28YQhkIqsxVdIQFm0VQ39RCM/DJjkPUNTZ3yAndPjGSQnE8k5GRQUZGhpnMLFB+9XceWNVhmcUm8yJ7Ul5UyYDh/agsqUZr6bpp1BtRA6KIHhhpCudgcDgcZGdnk5GRweLFi49K3cYup1IyNOeK2taADW9kZGSwcOFCysvLzbttIC6ZNIq3fzGLm84ZQ5TNglUIXvnJTNYvnuNVMFfVNbLm64Om/Tkc1DW2mKWubv2eTGq0YfcRThk5oE1O6PaJkRSdw+FwhOThoCqKHB3sdjuFhYWBhZYXa2d7wWxQWSILdyy75a1Qh9eGkh2l7Nmwv+OKICyxGRkZaJp2VM6zLgvnqWMGs37xHPPlz70uLi6ORYsWBd33e/n7ueLxXG55bi0NTS08eOVpDIiO8Fqg1XBzq6hp5MHUiV36Lf5obNZ4N6+YwXqIdkVtY5uc0P4y6SkC01vzGij843A4gnL9SzozIaj+rvjjhfTTXd0uW/z9kMbWnrPmTSZ2UDSxg9rlxw6vgh52RGe8K6ZNm6Zt3LjRd2dC+LQBGTHw/sKnje2r65q47cX1FJa2LYHV3qRRXdfEz1/aQGFpNfbYSA6U17E2Y07IHiNn/e4Dr+ssAlKnJrK9uKKDt4iia/g7Z3pie4VvwvHfPHfFvzq9nS3axv+98UOgc+65UVFR1Nd3LUNkd5+DQohNmqZN60y/PRLa5na7yc7ODio73ZxHVzNtzJAOghngX58WUNfQbE7CeU5KVtdLI7992Ci/niPB/IH+Jja/GjKSaQuX8ZcbpxJptVBd18Rm1xGVgF+h8Ibu0wxwwpkJFOUfoDlA0rQrHr7Q/NwZ3/Gu3kx6a9X0HhHO2dnZlJeXBwybBvj+xBEs37CXp+dN48qUKezb29YN5xPg7iD3OzIhkRP+70UzPweERxuoaWgxvUWsFkGkzcKwAVGkXzReJeAPE0czp4EifNgirdz/0UJKyg/6dYvz5M527Tqb3a6zGJGdXWV4XHwYR9NKjwhn48BmZWWRl5eHw+Hw6X4zyMO2u2/vnpAF6cxusAtff9YJXJycwMdfFvPpjkPsK6uhqKyWD7buZ8ZJQyk4VK0S8IeAkdPgiSeeaJPgRnHsMezEIZT8+2BYg9PCjSGf2pthUh+dA0D2vW1Ti/YbGsvEi05m2/vfUn24xmwXbrq3treOYWdevHixmYPVF//I2YVFQH6h7wihzhBls7RJmB8O3vh8D98dqOB/O0rYW1ZNs+76899tB3ltXSE2i1AJ+EPAbrcTFxcHyLDchQsXKg36KBBqrpARg0fQf1i/bhtfZ5LzG8EygVIGe94Hsu/9kG8+/A6AQUlx5vJYezSTrppAwhnyaXzouMFd/AX+6RHNOdgCrwD22AimjxvKG5/vCdw4CHaVVLE0dye3zurogmdg3DCcTicg04sGSorurmkgdWoig/pF8OjKr6nXnew1De668CSVgD9EDKEMsGbNGuUydxQI9oZ4cMchyotksrB1WZup88hvbgSZdAee2e5Kdx3x2c54+jLOIX+5ddor+Ds/dQFQVtjqBnxo5xGZI0Qn66Z/mxOY4DtF6vNzX74ReEVvFj3feZPfx+se0Zw7wx+uS2ayw7/vpJFkOy0tLWBWLIDPvvWfszUzMxOn08ncuXN54oknWLZsmd877IBoG9dOT2Lu1ATe+mIvNtH2H21q1lQC/hDJyspiwYIFJCUlqXp7vZz4U4Zx8vljOfn8scx76Rp+9PcrueiBFGYvmMmIU/2bFFNTU4MqAN2e9sn512dt9tk2PT29jYD29+Qe1b+tUhURHeF93H+ewwm6m6DnBCa03jS88IzPHXuh1yUiNiba2lcdMXA6naSnp5OSkmIKUX95o20WwWM3TvG7T7vdzty5c8nOzja1Z39316XzZ9DconH3SxvZU1pFbTun+mf/+x3rd5aqBPwhEiiaVNE76T+sn2nOOPn8sfCo93Zut9u8nvPy8oKuauItOX/loY7eXSAVL2P+wuFwUF5e7nc/9VUNAPwu91dyEtMHnpOW7ScwAUYOHcWima0//Ienz0sA+gF7gBN8duxBr9Ochw6IYtiAKJ+uaEYCEkMgB7IhZd48NaCJIT093TS7FBQUkJSU5PcxOtJqIa9Qpi6tqm/BiDaNjrDw0p0zWL94jhLMIeJ0OiksLAwq1F5xbJKens6CBQuYPXt2wOu4Sc/ffmjXYf5+9auU768kaXqCmYp0zFmjvW7n6YxgCOXJkyf73I8tSs5PlZQfDCk4qri0NSKxsa6J85JmxwNPA0GHFIdVOIc6gWAfPoqGpmYyrj6DSKv3oQV6LGlPbKQtKBODIYxzcnIC2jeveDyXvD1lJA1tG4BS19jCe3n7vUYyKjrH0qVLmTVrlrI1H6e4XC5ycnKYPXs2EFyJqUO7DrP8F++b3/9x7et8lytt2l+u3OF1m4yMDObNm4fL5aKwsJCkpCS/aT+b6v37YHcWrUUj96l1NMmY9fs8VgWclAqrcHa5XF2+02zcfZhT7vxnwFwdBoamG0hQ3/biehb9Z6vfNgZ5eXk4nc6Afa5fPIezTxxKYWnH5N2vry9U1VHCQHZ2dpdskYpjAyPLW0GBFK6etQC90dLcwsePftJmWeqjc7BG+BdhRt1Qo+9gzGRR/b3bmbvC3695jV2fFhJli7IAdcA4fVWFn82AXmRzNnJ1eCJ8ZLlzOp3mHTEYQRoMTqfTrBVmmEz89e1tvAqFIjhcLhf5+fmmR05eXp7fJEoF6/dSebCqTcRhe/9jf6SnpxMXFxfUU3d9lX/LQ2fKV1lsFlqaWnh967KCH54xbxHwFyAeuCXgtgF774UsXbrUTIQdLu1qwYIFlJeXM2nSJFwuF7feeqs5Oag4NlEZ73ovWVlZaJrG3LlzzWvOn3D+9LkNCIvost90VlYWbrc75AnmvLw8lixZEnR7I491UcW+OmAZUjBHzHfeFDDpSK/RnDtDcnIyeXl55qNvRkZGyAddBTkcf4SlbFqGgPlbYGRwngSKzhOMNtukz+NUlXT0yogaGEV9RfdH5Obl5ZGamsq8efPIzMwMWKLLk6sn3pAINABRQMPzc18WSCHtc4LqmBTORsJvxbFPqHkNur1i9YLdMNC7J4AidPy5wQZLTwhmkGaMwsJCrrzySrOASCCN38AxaKxnJWhDSPvlmBTOiuOHXv/E8ukjcMnTR3sUCm942J97goyMDNxuN/PmzTNNoF1kJXBNoEbHpM1ZoegqxqOoYVdOTk72P7ew6QX5UvQKrJEeIqudYB4eFx/SHEOg7HIOj9JVEFJdwaBU/T6nOff6x2hF6GT4/n+zs7PNasqpqakAzJo1y39/B4NzxVR0P3EjB3Kk0M0Fvz6XT1/YQF15PfHjh3JweykPzXqsbWMfmvVVj11MyXelfPPxTs67czq5T6+nbI/MnTHEYeewy39AjJGYK9iIRi9cr79bgWPT5twdgrTXP0YrQsOPYAY5qRMXFxdUThaT+DNCHFTfJpz5kn/20o875oYOMk80QOLoROafdBPf5hZweHcZ2fe0dccLJJgBMwzcyMfTBQy5exjoH6hRr0QJUkXQBBDKngRbaBiAaXfC1PldGJDCoDPX8Wd/38hXK7e3WSasglh7DNWHa8yw6q5i3CQSk0fy1TvbQYNRp8ez/0vfeTTak5KSEmpptGak1pzir5GyOSuOH+Y8Lt+tvifCjUfRQClhTS57Fqy9Woc5rpg091SueuxirnrsYrMg6+V/uDBgJGBneH7uy+Qt32Zmjrt08fcZMXG4ud4aFZ7871c9djFJ070WuG2Y77xJzHfe5LsgK0o4K44nPvyFfG/2Pd+SlpbGrFmzuPXWW5U7Zi+k/7B+DDtpCMNOGkKMPRqAt+/7iIoDVWHdT/G2EpobZYBIwfq9lO1xEzVAprsYFqbk+cNOGsK5d0znqscu5rFPH/4GeEdf9b1gtlfCWdErCDma70U9b/DomfJ92l0+92UELCmzWe/mvLumM3vBTGYvmEl0XLTftm63G4fDEfRcgvCQfF/8K4/hJw+lvlKmCz3w9aEuj7k9xs1m55EdNfOdN10ejMZsoJ7XFL2CsETzARTr5/3GZ722c7lcLF68mLi4uOCq87S3ZV/9Opx+vfe2irASf8ow4k+RN92Tz5dl3+4UN3tta/yXRvRwIE8Kx9mJFKzdC0D0gEjOveNMVj64yoxANNzyuko4vLpEZy6IadOmaRs3BiX0FYpOEY6q6JrzDti+Amqk5iMWE3qfi4BT5sJ5v5ULh0+EyFi/2ym6D2/nidvtZtCgQaxZs8bMQOcr5077Iq7eG+nvGlisgpZmDYtN0NLU8VyyRdvalKjyM+5NmqZNC9jQA2XWUBw/bH7BFMxhZYcT/nE2ONPC37ciZLKysswCGdnZ2WamO794SL7ZC2Zy1WMXm2WnYu3RpsY+7KQhAGY5rNQ/z2nTtn2JqnCizBqKXk1KSoppI7bb7Wa6xh5j4vVw6lWw+UXYvQqy0+C6N3t2DAq/GJ436enplJeXk5qayoIFC/ybrfRi3bZom2ky+cF9s/j3z1cy9Ydn8MmzXwBwpFD6PVsjpAfHsBOHYLFauPiB2d3zYzxQmrOi15KXl0dubi6LFy/uXNBIOLn2dTjtOrj6Nfm95MujMw6FT1JTUyksLMTpdJKUlMSaNWv8CmZfmu/2j74jakAU9ZX1puueIZQNS4rW0nPJPJTmrOi12O12kpKSAiZi70D/EVB1gKThA0Kb1EmIh1cuk4EoG5+TC4ef3uX+FN2DkaXSbreTmpoaMH3wsJOGeNV83fsrKdlRSsmOUnNZnZ7xbqdeDivrpn8HZWMOB0o4K3ot+fn5AOTm5gYXJnvje/DqJVB1AADXXZXB76x9zuYMARyE796VL4Dhp8GVLwXfp6LHcLlclJeXmzUJu8Kkuady8qwx1Lhr2bJ8Gwe/PsSQMYM4XFBG/IRhHPz6ULfamNujvDUUvQJvs/Aul4vk5GTKy8sD2hCFEGgvng0nnAdr/9J2ZcLZOH63g8IDZV0eX1JSkvKL7gWExasntNDrru63094aSnNW9FocDgdutxuXy8WYMWNIT0/3X3li33ookhM52B3gdsnPRespPBC6W53p86x8nRU9gBLOOgUlVfzuP1vZc7ia6AgrlyUn8PM5pxztYfV5cnJyTJ/VoMoCCQtoLa2COZz8WBf8wyeGv2+Foh1KOOvUN7Vw0RmjmHnSUP79xR5eWetixklDmTZ2yNEeWp8mMzOTvLw8FixYEFydyP4joGJf9wzmH2fD0PFw6Ou2y1WNQUU3oISzzvhRAxk/aiAA08YOZvmGvVTU+i+Rruh+OlVj7syfwEVL4PcR8rstFppqfDZ3Op088cQTuN1uc/KxoKDAt3autbQK5hm/hLN1n+v+/itoKMJHXyqWofyc21FV18g/cnYxenAsM08a1rU+Giq5a9V87CPtoSXzCbKyr0Ln0r/BwXwYf6X87ic7HcDSpUvJzc3F5XKxaNEi1qxZ4/+Yz36o9fOmF+HvZ8Enfwh93IqgcblcaJrW5dexNKmrNGcPquoaufulTVTUNPLXmyew8H8/oaTmIJHWSKbFn8nPJy8g0hoZsB+rxcbNp97CcwdeCE8yH0XwRPaX9mZLBET2gzrflS08k+Skp6cH9qXO0YVzzBC46QPY9SGsfkC62E3/afh+g0KB0pxNquuauPuljew7Us29c0/kkQ0PU1xZgs1iY1DUYHL35bDxwIag+oqxxTAz4ZxuHrGiAxkCnh4PjhS4K781WZEfjMiyoOrBac3QbzjUHoaiz+FsPYeDqjGo6Ab6nObscDgoLCz02+bj33hfvpL3gvJ3rWqoZMGan3dxhH2TkG2JIwbBjz+E+gp47yfwxVMQOQDik+Gg99BvT9e8W2+9NfBOMlpgz2fwz3PBlQuNtXK5qjGo6Ab6nOZs5A3u6iuQYAdp1rhlQprXdTk5OSQnJ5t25ZSUlKOXN6IXEbQtsWgj2qd/QVsE2iuXy/dF4JpfBstSYNsbcN1/ZJh1Uy1U7pMThV6w2+2kpaUxaNAggMD/w6FvYLtTft6+Aj57VPatagwquoE+pzmHg4bmBr+25+Kq/T7XGwLBsHPm5uYGlRxcoRPZH758VX7+9h2wRsKwCZBwNmx6TmaPGzEJ7tzcbsNnOnTldrvJzc1l3rx5TJo0KfB/8PwUuX/DK0TVFlR0I+rs6gIbD2zwa1Mubyjnmbynva5LTk4mOTmZjIwMXC4X2dnZXS2vfnzSvvJIex/ioadIwfvFM3DCOVJAr34Azriltc3BrUFV487Ly2PevHmUl5cH57L3QG2QP0KhCJ0+Z9bwRllZGffddx9Tp05FCMGgQYNYtWqVz/aj+nutqGsyefgUMmc/5bdNTk4O6enpSjB7Y87jsFCWEOL5yVLQZggo9jA7TP8JtDRBxX75PT+rdd2mF4LaTUpKCm63G03TlNuiotehhDOwe/duNm3axIUXXsi9997L2LFjuffee322/2VuOje8ex1/3fgXGpobOvbn3sVO93d+95mamuqzlE6fJ2ex9CEGuNAjiZEhqLe+Cp89BlUH4Zu35DqvHhPhKXGvUBwNlFkDmDp1Kh9//DEgBfWbb77J/Pm+J3maW5ppoIHcfTmcGT+d7yXOarO+vKGcF7Y+53efycnJnYt+Ow4IxlNGUq6/gMW/NpcmDYnCtc4JAxNg7WNQ+k1bG/DvI8Big+hBslyVNRKalSlCcWyihLMHu3fvZty4cUyZMoU77rjDZ7tzE87DXe9ma2k+R+qOANJ97tf/+5UZtDJ+0Hiv27rdbrKyssjOzu5cAvnjgLBU2N6+HC552suEn05LU2sdQSWYFccwyqzhwdixYxkzZgz33Xef6V7ljXXFa9laKnMxrNzt5Oq3U/nxR7cRFxnH4ylL+EHSHDaVbPK6rcvlIiMjA8B/jTOFdza94NumPFFP4zlgtHzvPwLHcwMRi+XXroTQR0dHI4Q4pnIyKI4PlHBuh2F39kdjS2tCJIHg8ZQlzHFcxNdHtlFUuY9hMcOwCe8PJcnJybjdbnJyctQkVFfxZl/evwlG6bnMqw/K96oDFB6sCMmvvb6+XvpRpxV2nJRUKLoRZdbQefTRRykrK+PNN9/E7fadjwHg9CGns6NsBw0tDZTUlvD05ifZXyO9Bh7Z8EcA+kX06/YxHy8Y/sWpqamkpaUFvmltegEc57dNeL97NXzyMAhrazXOcHF2Oky4DgaMgIGjw9u3QuGDPiOcDZuwLx555BHcbjcXXHABzz//vN++th3ehs3Seuh2uLcDkNAvgYQBo/niwOdUN1aHZ+DHOU6n00zXmZ+f3ya5vlfik+HSZ2SgSZvlp0Fzo8x/gZCBKe3zLneV9Zmw8XmYdLO0dysUPUCfMWsYmeJ8UVZWhqZpfPzxx4wdO9ZvXxoaDS3ShW5EzAhAmjeKqov4ruzb8A26DzBr1izi4uKYNWsWmqYFdi8s2Qorf9xx+SuXyHBtADQY9wOfXRi1CT1ty6mpqf7321Tr397tieGX7c0/W6EIkj4jnMOZKU5DI6FfAgLBgdoDxEXaOWWw9M4oq5dFRCMtkSSekBhSPue+MAllt9tNO3xmZmZAk5KZ8P51L8L03Ptg2ESwRkH+v3zm1MjPzyc/P5958+aRnZ3NmjVryMrKCm7AwWagMwJpFu5VZa0UXaLPCGeAbaVfha2vouoiBkQOAKC8wU1BeQEW0Xo4G1oa+MXy9D6TGDwUUlJSyM/PZ+HChYE12PnSBELF3o7rPn0EDm2TSfZrD8OU2712UVYmb6DJyckkJSWRkpISvFtjsBnojEAalYxf0UX6lHA+cdBJYeln5ZXvsfLK93jl0tdZeeV7/O+uT2lobqBFa2Fo9FCzXX2AShwKSUZGBmvWrGHBggVmIiifPK/bmku3tzUZXPI3+X7ixfJ99u99aqxG/wsXLmTy5MnBac3WKJh2Z3AZ6C75G9yaKzX3jc8FHU6uUHjSZ4Tzbvcuvjn8NaMSR4Vsamiv4ZYfqCBKz0JXWldq7vPyMVccrZ97TOF2u83kQ3FxccG5GH7vwbYmg+m6CWPn+/J9zYNwwLuQT0tLM8tSzZ07l/T0dP/7uuRpeLAOLns2uEx0038iJyxVMn5FCPQZb43yhnKe3vIk05+aSos2GYBYWyxT46fx6+n3hhS5BnDPmb/hi+L1fFD4vrks/3AeT+U/2aVSV32J9PR0li1bBsi6fkGZGNb9Ff73e/9ttv3b62IjM6Db7Q5s4wZ472fyFUyV7f2boGANnHwpfPuuXKaS8Su6QJ/RnCcPn8KS2U8xot9IbBYbUdYo6pvr+V9Rblj6/8PnD/HJ/v8xfcRZTB42BYDC8kJuPvUWnjr/GX6QNKdTpa76EllZWeZTSFpamv/GRvHWMefL91Ov6tjGot/8Srf77CYnJ4eUlBRyc3MDR2rO/n3wE3tGvunnp6hk/IqQ6DOaM7S60znixvCh631W7FxO/4j+Xtu6XC4yMzPJysrCbrebARK+ErI3a81UN1bzxYHPibJEATBpeLLpITIsZhgRloiA6UZD4UB1MT9ZdSdNWhMAw2OHk5nyFP0jvf/GYxLXGinwvv8neCRO5nMGEBbpySGsoLs58u1K7124XMyePZu4uDgWLVoU+Iaw4RmoLIKLnww8PiPftEIRIn1GOLdPTDQ4ajBWYeUXU3/Fa7zZob3L5TJzLrvdbpYsWWK6fXnj7BEz2XjwC5q0JjQ0Pv/ZBlYWXdqh3XKcQY03mFqF7YmyRnHZ2CuYEj+Vl77OYqf7O97c8Qa3nf5/neqnVzPpFvj8SSkwAYyUrVqL/t7c2nbkVKBjjhOHw9E5M9b0n6oq24oep8+YNQyt+e7J6dQ21bK3Srpi/WG9d7ulUdvP4XCYE1X+3LzumX4v00fIHMS/mPorSooOdXutwvYMih7Mbaf/H8nDkxkeMxyA8YO9Z8c7ZnHpZqiIflJQLyiACx+Tywa0eyop9p58qtOsfkC+v/czFVSi6DH6jOZsBKHUN9dzTcU1vLXzLZq1Zm4/fT7ZeH/8Bcycy4ag9sVVb8uKJv0jBnDqkFPDOvbO8M6ulbzwpcwlPSDy6I6lPSFX2B4SBZNvgw8WAAK2Z0NlMVgiZIMaw1NGABoMPRX4JsRRe5CyWO6/f3z4+lQofCA683g3bdo0bePGjd04nO7lY9dHPJv/N5q0Jkb2G0lxdTFWYSU7daXPx1wj1DclJcVncnwhBH9c/zDritfK7wjevvLdkHMXd2X7ioYKvj2yg+xdK9h6KJ8ZI2fym7N+2+VxdDulO+CtG1oT5yecBbtWQYvuI95/JFy/QrrI5S6GxHPg9Btlwv137oKq4o592mJkuHXEAMQDlaH/D4sABPQfAePnStuzNaLLfSr6HkKITZqmTevMNse95uxpa7YIi2FNJZYAACAASURBVHxpFoqrixndP5HfTL/fr+bscDjM6tjGDL83hkQPQSDQ0JgafyZv826HNkaIclZWFm63O6A23ln+tzeXQ7UlnDzoFGKs0QBE26LD1n+3YEygtXdB+/jXcM49EDsMogfKqiYGhk/z2Avhj3r2P4sNogbC6BmQOBOa6qChiqQhz4SmrcfHIauyaDBqqgwqUbZnRQ9w3Atnw9bc0NzAs1v/Rq1HdYySmoO8+e0bXrdzu93k5uZSVlZGZmYm5eXlfifo3iloFfARFu+169xuN9nZ2aSmppKdnU1WVpaZeD8cHKw9yCvf/IsWrQWBIGlAEndNOkaEiOGCtubB1tJTI6fChwuloDbY+xl88AuYenurHzHICii1R+C7d+ULINqO6+lLYPsKOPEi2PlBcGOZ8zhMuFYf1wB4VPe77ift+CqoRNETHPfC2bA1ry9eZz7eWoWVZq2ZhhZZB9AbbrebJ554wvw+d+7cwC5XOuuK13ldbmjhIG3YQQVAeOD5FBBhiUBDo6mlqU2Ay7UnX9epPsNJ8DUCvSM9VDxKS5XugH7xULlfTgBOuFpOAr50AY6bnqHwcKDweDewQv/cVjAnDYnC9bN22592A3z1Gqz5Hax+EAafKLVkg1pZkkwFlSh6guNeOINMePTYhj/T0NLA5OFTmDhkIq9tf5XZo8/nv3tXed3G4XB0qjq2RVhMjdUfLpfL9PoIpkxVe4HsGDiG+6bfz0euD1i5+23mn34nJbUlrNi5nBkjZ4Yt815XCEuNQE8Mk0eGkGaL3asgIlbu63B9ePcFUjCfc480i7x9OxzMly+EfO35VAWVKHqM41Y4txdqk4ZNYkBkHKv3rmJLyWYEglV7P2ZE7Iiw7K9F97PV0Ejo77tahtvtxuVyUV5eTlZWll9tvKG5wWvgTFHlPkb2G0mEJYKJQ09HK93a7QEuR5VL/gYnnCMDTgy3tnAirK3+0Z/9Wb7fthb+OROm3gGX+y++oFB0B8ett0ZtUy3rij7jpW9eoqzuCBoaw2OGU1Jbwpnx09lw8Auz7cor3wtZC7t8xSUkDUiisLLQZ5+GGcNut5OSkoLL5fJpxxZC8Nm+TxlrH9sm6g/AZrHR1NLESfaTcVW4aNSfCO6bfj8xtpgu/45Q6aqHSdDbN9SYE4BiMWHywgiCYHJqKBR+6Iq3xnEbhBJji2FGwjlceMKFpsAqqS1hbNw4BkXLytpThk8N6z6Hxgzzuz43N5eUlBScTidpaWkUFhb6tTuP6p9AlDWKs0fObFMW69yR5/GjU2/mO/e3pI5L5Uen3syWks2sKvw4bL8lXLjdbhwOh+mOmJGRgdMZXJQkIL04PnsMDn3TGhXoB2OSNSUlpYueMLq5wzPIRSXLVxwFjlvhDDJN6Iqdy6lpqjHzLLsqCvi48CMEgh2HZWKcuBEDQ0ojGjdiIACbSjYSaYnkIsclXscza9YsUlJSeOKJJ8jMzGTBggV+M7DF94tnUPRgFkxN58nZTxOvl8Rq0BqobqgCINoWQ7RV5vKI0t97E3l5eRQWFpqCMjMzk4KCguA7aJ9IaMI1cMpcr02dTie33nqracsPmArUG8ZNUGuWNu7PHu18HwpFGDhubc4gk+v/NPnnPLklk9K6UhL6j+b3Mx/mnk9+RWltKdXN1ViwMP/1OzhYe5AjdYfNbW3CRuKARAoqWgXJ4OjBHKk7gkVYiLZGMyR6CHur9nLfmfdzxrAz+GXuLyiu3s8Hrve8jsdutwc1CWjwQcH7TBo2iY8KP+K9gnfM5euL1xFjiyHaGsNr21+hf2R/LhlzGeef8P0uHKXuxZhUdTqdTJo0CZfLFXzVEeiYSMgIWvHCli1bADmZa2jPfrFGSWE8aCyUfCmXjb1ABrwMHS9vBCqnhuIocdxqzkZy/XH2cVwyRiYgKqrax52r7iA+Jp6nZj/DlSdeRQstnDH8DKoaKgGItkZzsv0UmrQmiqr3m/3F2GIoq5PljSYMnsgFSReyt2ovVmFlVP8ErBYbN4y/kfmn38nYuHFh+Q3F1cWUN5TzefF6LMKCTch76Znx03nt0jd58/K3WD7XyUsXv8Kdk+5qY/roLaSmprJgwQKeeOIJXC5X8LX6fNFQJSMEvZCWlsbcuXPNDIIBS15d+4YMXDEE86Bx0lMDpFlDJctXHEV639UcJsobysnc9DgVDRXE2mJJ6D+aoqp9TBxyGlsObWaX+ztW71kNwBs7Xgcg2hrD7895mJy9a/jWvYMGvczUmIFj2VO5Bw0Nq7Dy1eEv+eqwvKDH2U8kvl88MbYYUhJnA9JjYwlPhfwb5jjmUFRZxGVjL+PkQafw9q5sPj/wObG6O9mxQHJysvm0YHiqdJkM/26KRpIqkKWv7HY7brfbt6Z++Du48R14QZ97KC+ET/6odzar1cat/JoVR4HjVjhPHj6F9Km/4OktT1JWV4bVYuWSMZcxst9IvizdylVnX0P5gfIO2/2b/wTVf9yIgXzv2XP5tmwH7+5+h//uWcWB6mKaPVNWhkiMNYbntz5HZWOFueyEASccO1F/Oi6XC4fDgcPhCF1zPmUunPdbWDw9YFO73U5ubi5z53q3UbPln/Df38jPEf1klKEtCiL7wbrHWyMVlV+z4ihw3ApnkAL6H3OyABmIsmjtgzS0NDBpWDIrDrwdtiAGm5C+yKP6J7Diu/+wZt9qoqKiQsrpEBUVxcKcBTS0NBBrizXt2zeOv6n358tox5gxY1i0aBE5OTmdszd7Y4fTZxJ9t9ttasyGrXvWrFm++3IXQLQdJl4HFy0Jrj6gQtFD9Jmz8cRBJ5E5+0ne2bWS91wdkxKFwrKvlxJpjWRUv1EUVRUBUF8fegTblPip3Dxhnhl8Yti3jzVmzZpFVlYWDoejax4UBhOvl2WpNr8IdIzsNMwYWVlZJCcnk52d7f9m8ECt73UKxVGmTwjn3e5dVDRUMKLfCJppCWvfFizcP/0BVuxcbtqh/fHoo49y3333MWbMGDZt2sSgQYN8tv3x6XdQUC7dAUEGXby54zXSp/5SFoltb4PtpcESnQmD98u1cm6AMefDPO8+5SGbTRSKXsJxK5zbpwoFaG5pNt3OVuLd3S0jI8O8wLOysgK6YyUMGM1pw06nqHof28u+4QdJF7Vxe/Pkvvvu48033+T5559n/vz5bNq0iQsuuMBn33euup36ZiM5j6CFFj7d/ylH6sp46JyHiYS2GdSO5yTw+S/DV6/DtDtl2k6F4jjnuBXO3nJS3Hfm/WZioLv4SYdtnE4nOTk5ZGVlkZeXR0ZGhl+t72T7KXzr3sEN715Hi9bChCETaW5p8tn+jjvu4N5772Xq1KmMGTPGr2AGPAQzgDSRCARfH9nGxgMbmAmQsxjWPganXBFcAdJjldghULBaTwfadVu+QnGscNz6ORupQkf1HxV05eu4uDiz2nZWVhYLFy70296lB6icN+p7xNpi+frwNnL35fC9hBSv7ceOHUtZ2f+3d+/RUZdnAse/v5lkkswEJoNggAQSgmhX7TIQvFA9EruAoqclakEr7jqohdZbYl0N66VEUCv1FqyiUJWh9VJA27BbQYU9xK3WS4MZxHqpBgZIACVkZsjkOpP57R+/mZ8JzEyukCE8n3NyCMn7u8Rz8vD6vs/7PB527drF+PHj8Xg8ce8/c+wlJCudO25ENhmXVz3J41Nvou36LVpGQeVzsG1V3Pud0CbMgvuaoFSF0v5dmhIiEQ3a4AxahsZP/vsKVu1YydnDv0+mJf7/9kfqMUyaNAm32x1/px9oC2mdn2saaphz+tUAXJJ7KdedeV3Ma/Ly8qivr6e+vp7p06fHDdB/rf0/AmoAgORwn7yQGiLTPJLpY2fwTvM+KtXWhDksEekR2NuPnJychHyWEANhUAfnSIZGTwsDVVVV4XA4sNvtcQsTTcvSgnf14a957Z/rOHfkebxX+y43b/l53PvbbDa2bNnC+PHjWbYsdu2GjssagVBAPyH4TdMB6jxfk4yB0YHWhDks4Xa7+9RxvCcHVKI+6+AXqM9OQl2airpsOOpfbkYNBvr8LCEGwqBdc+6YodHTwkCRU225ubmUlZXFbCX1Tu07et/AJT94iAnDJujfi1V0f9GiRfqMefPmzcydG7tziVExYk4y4w9oRY6CahBLkoXGYCPvez5jUmsrma/8WGtoKocljq7DIcQJbFAF52gZGh1nn0+7nuJp11MYleg9/qBz+6iuiuEbMBAKp+bVNtZ0Cs6x2Gw2FixYAMDcuXNZuTJ2IfeAGiAQ0JY1Iv8IBENBLsoqIGtIFq9+8TJbrn6FH43/cZfPFUKcWAZFcO5t77rI5prWu84NoNcbtlqtrFmzJvbRX9CSBsLnTFZ98hy/rVqu9/OLpaSkhJKSkh6/q4pKRkoGv5h4M+OseXy4/wMgMcuECiH6blAE5/7sXRcpnNMdkdZUADlDcqhrOcTBpm9jNo3t1buhcOeUu9nfuI+XP/8DT1UtpzXYmtBlQoUQfTcogvNAMWJEURSCapBxGeP50Smz2eXbyfqv1vXbM1RUdvqqsaVox5Dnn3UjM3Mv6bf7CyESkwTnPjAYDARC360JP7HtMdpCbWTHafDaE6dZJ/C17yv+/NXrWFOsMlMW4iQyqFPpvF4vxcXFZGRk9KxvXTeFQiFSjVqFuGGpNm6x34ZRMVLjr8E2ytbH1ldWavx7Afj5xJsTuqC+EKL/DerfdLvdjt1uZ82aNaxevTru5l5Py3tmjcni2Rkr+XD/B7zw6fN4W72cM/JcCk+7gte/eo0LV0xlSuY5fPztNkJqCAWFsUPG8ui0J+KW/Kz69mO9BnVqUio/HDudGTkze/RuQogTn9KTjbQpU6aolZWVx/B1ekdRlKM2BCsqKigsLMTtduNyuXA4HDEPHkS7Pp6OATTdlM6EjDP42vtPDrcdxpJsISs9m8/rP+NW++2yPiyEQFGUbaqqTunJNYN25lxQUNCpC3OX/eR6oGMR/4iOAXt/4z5ZHxZC9MmgDc6AfoDEbrdTUVERv59cH0UL2EII0VuDekMwwul0UlhY2LcuHEIIcRwN6plzQUEBdrudwsJCCgoK+nVpQwghjqVBPXMuLy/H6/Vy8cUX6wE6XpU5IYRIFIM2W+N4Xi+EEPH0JltjUM+chRDiRCXBWQghEpAEZyGESECDIlsj0k+uL9cLIUQiGRTBWfrBCSEGG1nWEOIk4Xc6OXDOeey3T+bwo49JhlKCGxQzZyEGO7/Tif+ZZ1EDASzzrsUwfDj+Fc8SOnwYFFBS07DMu5Yh/3nnUUt8fqeThieXE6qrw3ThBZjnzMFbVMz3n3icPQcO9PqdOrZ3E/1PZs5CJLi2Tz7Bd+/9WOY7sN5/Hw1ly/Hddz+pl16C6vejNvhJ+/GPaChbTsumN6Nem/S972l/f/c9lLQ0FLOZPQcOoKpqrz9607dTdJ/MnIVIcC1vbwbAfM3VGGw2PL+8E4JBMJm0AWlptHt9KGYzDStW4Ftcqs+wIwsXxlEj9bEtb29GsViO/w8iekSCsxAJrv1gHQAGi0VbskhKgmAQ1XcYAMViQT1UBykmAlUuht57D8bMTDy3F5Fy0UUAGEdqwVkxm2k/+K22HCISmixrCJHgjCOGA7DvtNOpzRoDLS0ABPfuAUCtqyN44Bs9WDc8t5JAdTWK2UxrVRUA/t8+rY31+VCbmqG1NebzXC4Xubm5ej10MTBk5ixEglMNBqZ669nb1NT5G+vWfvf5vprOn5ds7zR0zJAhvD/ECsEgge3bUUaM6HxNB4WFhezevZuysjLcbnfcIF2bPRbTuedwymvrMRhkrtef5L+mEAmu7d332NvU1KfNu70NDdrN0tKgrQ1DWlrUZ1VUVOgbfXa7HafTicvlivlu5uvm0fbhRxy69lpJ0+tnEpyFSHCh+vr+uZGioKSmopjNEGOW6/P5sFqtrF69mvLycv0jFuvDDwHQ9tf3CDU0oLa00FC2nOaNm/rnnU9iEpyFSHQpKf1zH1UFRUFtaaG9JvqSRlVVFcXFxXqLt650XMqw3HQj1geXAtC42hl1vByE6T4JzkIkmCMDmJ4GF4XL5dK7/XRnA0+trweDgeRJk6J+f9KkSZ2WMdxuN7m5uTHvFwqF9M8t183DfNWVAAR37jxqbLR87SPzssV3JDgLkUCiBbD2Xbtiji8tLWX79u1s2LCBO+64I/6JvSFDtD+DQYJffhl1yOzZs3G73TgcDjZs2EBxcTE2my3mLX3/de93f+kQqKPpmK+dduUVKGYzzW+9Hfeak5kEZyESyOHfPAaA/7mVBKp3QkoKwa+ro46tqKhgw4YNFBUV4fF4uq6uGAhof5pM2hJHDOXl5bjdboqKiiguLmb27Nkxxza99BIkJwPwzbSLOXTjTQCEPJ6jli6OzNdWLBZCdQfjv/NJTFLphEgAfqeThrKnCB3UgtWQexbhu/Ou704BxhFZzujyOHVbGwCW+Q6SzvwXmDMn6rDc3FwqKiq69+JGox70lVCI1vBMOO3yy0m9uADP7UUkn3UWaZfN0vO1Q34/BpMJ1e/HMHxE955zEpKZsxADTK9/ccYZHb6qaFkV4YAaTUFBAYDetHjatGk4nc7YD0pPByC4Zw8Kva9/3pGSkQGAYcwYlA7LH0MfWKwvXTQ8s4J9Z55NQ9lyAA794maaXv8TanMzaTOm98t7DEYSnIUYYJG12MgRa4Am5xptVtoFq9VKaWkpoAXpeJt3kZOFrZvexHt3Sa/ft6OsHduZ6q0ndOgQI//+IaSmAmBMT9eOmptMBFwuVJ+P5LPPAiDw3t/w3nU3lltvIfXyy/rlPQYjWdYQYoBF1mIjWRmKxULgyy/jzpojHA6Hnofs9XopLCyMPThyv1NOgUOH+vbSYaqqakG4qYmmjRu1gkxAw/PP07h6DWp4Vg8Q2PGptj4dDEJbG02//wOGpKSoZU6FzJyFGHCRtdiUqecDoLa1YbJP7Na1xcXF+hqx0+kkI7zMEJfPB8AYs1nbmOvlx5EbkP7fPPZdcH74EUznnHPUoxWzWd+MVJua5MBKHBKchRhgqdP/DYDAPz4j7aqrIBAg8PkXJIWXAeKJBGaXy6WvQXfFEE6pez9jGG21tQC9OhJ+ZNpesLqa9NtuxVQwDYC2d98l+YgArfr92idGI0mnnQbA4Yd/LQdTopDgLMQAM9ntWJcuofHF1bS+8w7pt93KqM//QdqMGf3+LMtNN5L5iUtfz256+ZWYY71eLxkZGdjtdn3TsSuK0YgxKxuAkZUfkVH6q84D2tu1Pw0GfQ263e0m5PNhmjxZDqZ0IMFZiASQfsN8RlZ+xKjtVVgXlaAoij6jNpx6ar89x5iZScufy/Ug2fjCizHHOp1OfD4fXq+3e+VDFYWGsuW0ffQRoKXMJU+cqK0zJx2xvRUIdArCQ++9h5a33oKUFDmYEibBWYgEFZlRh7o5a+2Oww89jOf2Igxjx6IMHYra2Bh1XGlpKeXl5RQVFVFRUaEfTInHkDUaTCbav/oKgG+mXkD9jT+DQIBhzzyNdekSSA3XCTEYUJub9WvNV8/V1qNBDqaESXAWIgFF6ms0PPVbjGPG9Hnzbkw48EWE9uwhdcZ0bGVPRn1+ZDmjtLSU3NxciouLuzyYkjRmLJi1UqSGUaMgKYmWt94iddYsUi+/jPQb5pMyTVuPxmjsdEpRbWwEsxkCATmYEibBWYgEEzmUEvL7tQpy1dW8nzGMfZPy2fevdmrG5FAzOpvDz6yg8bXXqRmdTdMbG1FVFd+jj1EzOhvPA0uoGZ2tj30/Y5h28w4pa6HWNtKuvCLqO4wbNw5Az/6w2+1x6zoDGDJPBb82E0/KG8eof+xAMZtRIu21gNC+/dpr2Gwoaan6tY1/XIvq80EoJAdTwiQ4C3Gc5ObmdmuWmzJxItn7asj64jOyvvyc7H01ZO+r4fyaPVh/db++Xmy+5mqa39bWZ+t/toCDV16F6Yc/1B4WyWmObMBFdFj7DVZ/TWDHjqjvOnv2bJxOp76UUV5e3mVwbt+7R0+lC4XzqI+snxGZK5/6xv9wSqSTS4oJ/4pnIRAgddalcjAlTIKzEMfJ7t27+9TNZM/Bg/iWLNVT7Fre3kzLX96A5GQMo0bR9uFHtG7ciHXpElrefEvrsB1lIy5CbfDH3XxzOBzY7XbsdjsPPPBAlzWeA5Uf60e4g198ieeuuwnV1dH6/gd6ipwp/O6NL79CsDpcVlQFJSmJ9NtuZdjvVsmBlDAJzkKcQEZtr9JT7BojaXBGA6YfTAWgeeMmPfNj9D+/wDznJ7Fv1tZG20d/j/ntsrIycnNz8Xq9LF68OG5wTjrzTO2TDjP15lf/CKqK+dqf6ilyluvmAeAvW463qBgAa9mTnbJUhEaCsxAnmEiKXXukCl1LK+aZM7Wv7dnT+TCHMTxz7ljdzmDQOqI0NBDoYqnC5XLhdrv1+h2xBKu1sqbDfr/mqO+Zzj9fr90cCI/DZNI7vAQ/+STuvU9WUltDiAHmcDioqKjQsyPsdnvc8ZEUO98SrSWUed61KFlZAChWK9b778NzexHff+Jx9hw4cMzfH9AzLw5dfY32d0XRvpaWRuvmLfrac9PLrwJw6pa3MY4bx/4xObRsepNDtbW0bNwEoZB0847oyZpXfn6+KoToHe3XrbOtW7eqgDpx4kR19erVak5OjurxeLp1vX/tWrVmdLZ68Lp/Vw9cOkutGZ2tehb9lxoKhdTa006P+rzuvlc8OTk5KtreXq8+xpjNarvPp6qqqtZkjVFrJ5yh/xz1JYvUmtHZqnfpgz16p0QHVKo93GM4yf9pEiIxlJeX6xtw8Woyd+wv2L57D6mXzaJ1awXBT7SsCzU93Ioqpesi/b3V143NvU1NtH/zjdZ/UFVRwxke1tLFejdvKYYka85CDKjIwY5IHeaMjAw9xziaI/sLmiZPBlXFdOGFADStWEHzxk2oDf5uv0NOTk6PDrT0B9+Spfju0foPKmnawRXjiBHaUoaiEDp8uF+ecyKT4CxEAqmoqMBqtcYd43/hRXxLH4SkJBpf/xMAQ275hfbN5GT8v3tezzeOZv369eTl5bFw4UI8Hg9utzvqDBfgwIyZ7Js4if1TL6BmdDbBurp++TlbK96h6aWXSZ6Sj/H0CQCdZtOGoUP75TknMgnOQiQIr9fL7t27u9wQtFz/H1jvvw+CQULhDb+U887Tale0txP4+OO4vQdLSkrYtWsXHo+HYcOGsX79+phjQw1+Ql4v7eHnHJhybsyxxcXF2O12FEVhw4YNcX+GrL27yarZw6kbykn/qbaJ2HE2nXbJzLjXnwwkOAsxgDr2AXQ4HFit1i4L5qddNovUK8IdT0Ih7Q+/H8t8h1YBrr0dy7xro167bds2du3aRXV1NevWrWP69Ols27Yt5rNC336rHVxRtefE6s7icrlYvnw5hYWFbN26lcWLF8f9GTqyzJ1L6uWXdZpND7n/vm5fP1hJKp0QA6igoIDZs2djs9mYNm1at7peN2/cRFI4dc4wbBjtPh9Na9dhzMyE1lYAhvzyDnjowaOu9Xg8TJ48mby8PADy8/PjPyzcd9B0wQW0ba2IOSN3uVxcf/31FBcXk5GRQUFBAW63O35Pww5OWbWyW+NOJhKchRhgkR6A3dW42ona3g5JSZjyJ2O6YT7+Fc+iBgIknX02wU8/xWCxRL3W4/Ewo0MRf4/Hg61D1+xYDJnh5rMxNgRtNhsOh0Of9RcWFuJ0Ors8vCJik2UNIU4w6T+7iYzFv4JgkJY3NuJbXErI58M8b55+ejDkj56tYbPZ9GUMj8dDZWVlt4Kz0RreoOtQm6OjqqqqXvwkIh6ZOQuRQHJzc9kdOZYdRfa+Grjl5ujfLLmLsSNG8LfkFJrWros6JC8vj8rKStavX69vBJaUlHT5Xmp4uSSyxh3tvY8sxt/d1lYiOpk5C5FA+qNyXaQfYTR5eXksW7ZMD8jxNgMjUmfOoLk8nH0RrodxJIfDQVlZmR6gXS5X9zqBi5hk5izEcRI57HGshQ4dIvPvH2oFjqJYsGABCxYs6Na9kidPpmXL/2q5x2PHassau6qjji0sLMThcOD1enG73V3WfxbxSXAW4jjpqgcf0C/Bu6FsOclnndXn+wAEPv6Yoffeg9rWSsOjj5N21ZWwLXqZUdn861+yrCHECWDZsmUsWrSI/Px8Vq1aFXdspDxnfzFfczVqQDtxqMbYEBT9T4KzEAnO4/HwyCOP6OvDCxcujDv+yNZQfWWwWAjVaW2nVNnkO24kOAuR4Hbu3InX62Xz5s1s27aNOXPmxD1yrfr9GIaP6HFBoyM/xg4fDmhpeYbhpwBavWhxfEhwFiLBeTyeTht4eXl5cXOT1eZm0mZM1wsaRTpyB+vqCIVC1OSMo3b8BL0zd6C2Vvv66Gz2TcrXMz++2rwZgKa161CMRgCU5GTGZo7sU9DPyck5tv/BBgkJzkIkuC1btnQKxpGaGLGk33Zrpw7WkYMpTWvX0fynP0MggK3sSZIjff9ibEJGOq40vriaxjW/J+Wii2j92/t8kDkS768fIRQK9Srdrzsbo0KyNYRIeDabjZ07tU7VCxcuZPz48XHHWxd1PlQSCbKRI96R4N1SUUFgxw4aX3qZpHAN6aQjakmn3zCf9Bvm9+NPI7pLgrMQCW7OnDnk5+fj8XjYvHlzl9ka0UQLspbr5tH06h9pfOFFvQegxXF9v7yz6DslUlS7W4MV5SAQ+2ypEKKv8qP9Ti5cuJBVq1axcuXKuAdIwnnSXR/7C7stfcip8y3pI5MVlPKm5rpfHfbWdj8iiB7IUVV1RE8u6FFwFkIcW4qiqH35nVQUBVVVj/0xRHHMyYagEEIkIAnOQgiRgCQ4CyFEApJsDSESSGpq6jeKomT25fr+fB8xcGRDUAghEpAsawghRAKS4CyEEAlIgrMQQiQgCc5CCJGA/h80cFSJmNahmAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"Computing t-SNE embedding\")\n",
    "tsne = manifold.TSNE(n_components=2, init='pca', random_state=0)\n",
    "t0 = time()\n",
    "X_tsne = tsne.fit_transform(X)\n",
    "\n",
    "plot_embedding(X_tsne,\n",
    "               \"t-SNE embedding of the digits (time %.2fs)\" %\n",
    "               (time() - t0))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
